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• W4297103441 endingPage "105720" @default.
• W4297103441 startingPage "105720" @default.
• W4297103441 abstract "Accurate state-of-charge (SOC) estimation is critical to the energy management of lithium-ion batteries, which are increasingly employed in electric vehicles (EVs). In this work, a novel SOC estimation method of a battery based on voltage and stress measurements is proposed with the utilization of machine learning techniques. To evaluate the necessity of stress measurements for SOC estimation, a series of stress characteristic experiments are designed to probe the relationship between mechanical responses and the SOC. On this basis, the stress is taken as an auxiliary for the SOC estimation. Then, a typical long short-term memory (LSTM) network is introduced to map battery measurement signals directly to the battery SOC, where the voltage, stress, and current are selected as input vectors. The proposed method is evaluated under constant current (CC), dynamic stress test (DST), and urban dynamometer driving schedule (UDDS) conditions. Experimental results show that the maximum absolute error (MAE) and root mean square error (RMSE) can be bounded within 0.34 % and 0.45 %, which demonstrates that the voltage-stress-based method can achieve good SOC estimation accuracy. Therefore, the proposed method provides new insight into the SOC estimation of pouch lithium-ion batteries. • An in-situ stress tester is developed for pouch-type lithium-ion batteries. • Comprehensive stress characteristic experiments are designed. • A novel voltage-stress-based SOC estimation using a LSTM network is presented." @default.
• W4297103441 created "2022-09-27" @default.
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• W4297103441 date "2022-11-01" @default.
• W4297103441 modified "2023-10-12" @default.
• W4297103441 title "Voltage-stress-based state of charge estimation of pouch lithium-ion batteries using a long short-term memory network" @default.
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• W4297103441 doi "https://doi.org/10.1016/j.est.2022.105720" @default.
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